Getting Close: Proximity of the Hands Affects Target Prioritization and Movement Execution in the Gaze Cueing Paradigm
Bibliographic record
Abstract
Humans are influenced by social cues. Previous research has demonstrated that eye gaze can alter target prioritization (reaction time, RT). Specifically, when cued gaze direction is congruent with target locations, there is an increase in prioritization (facilitation) at short cue-target asynchronies and a decrease in prioritization (inhibition) in manual reaching tasks. Previous research has also suggested that when action was implied by the hand position of the gaze model relative to the targets, manual aiming movements were impacted. Specifically, movement trajectories were differentially affected when the gaze model appeared prepared to grasp the targets versus having their arms crossed. However, this previous work did not disassociate the model’s action potential from their hands’ proximity to the target. Thus, the present work aimed to disentangle a proximity-based account and an action potential-based account and their effects on movement execution. The same gaze model as in previous studies was used, positioned with his arms extended beneath the targets and hands facing downward (so there was no potential to act). Participants performed manual reaching tasks while following a nonpredictive gaze cue, including three variations in stimulus onset asynchrony (SOA). RT analysis showed a facilitation effect at 350 ms SOA, but no cueing effects at 100 ms or 850 ms SOA. Trajectory analysis showed similar facilitation effects at 350 SOA, but no significant deviations at other SOAs. The results suggest that the proximity of the hands may elicit the facilitation effect in movement execution, instead of the potential to act on the targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".